Search Results for author:
Found 9 papers, 1 papers with code
What Makes Good Collaborative Views? Contrastive Mutual Information Maximization for Multi-Agent Perception
The core philosophy of CMiMC is to preserve discriminative information of individual views in the collaborative view by maximizing mutual information between pre- and post-collaboration features while enhancing the efficacy of collaborative views by minimizing the loss function of downstream tasks.
How To Prevent the Poor Performance Clients for Personalized Federated Learning?
Intuitively, these poor clients may come from biased universal information shared with others.
Automated Customization of On-Thing Inference for Quality-of-Experience Enhancement
The core of our method is a DNN selection module that learns user QoE patterns on-the-fly and identifies the best-fit DNN for on-thing inference with the learned knowledge.
Context-Aware Online Client Selection for Hierarchical Federated Learning
In addition, client selection for HFL faces more challenges than conventional FL, e. g., the time-varying connection of client-ES pairs and the limited budget of the Network Operator (NO).
Autodidactic Neurosurgeon: Collaborative Deep Inference for Mobile Edge Intelligence via Online Learning
The basic idea of this system is to partition a deep neural network (DNN) into a front-end part running on the mobile device and a back-end part running on the edge server, with the key challenge being how to locate the optimal partition point to minimize the end-to-end inference delay.
Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks
For cooperative FL service providers, we design a distributed bandwidth allocation algorithm to optimize the overall performance of multiple FL services, meanwhile cater to the fairness among FL services and the privacy of clients.
Contextual Combinatorial Multi-armed Bandits with Volatile Arms and Submodular Reward
In this paper, we study the stochastic contextual combinatorial multi-armed bandit (CC-MAB) framework that is tailored for volatile arms and submodular reward functions.
Spatio-temporal Edge Service Placement: A Bandit Learning Approach
To solve this problem and optimize the edge computing performance, we propose SEEN, a Spatial-temporal Edge sErvice placemeNt algorithm.
Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing
Mobile edge computing (a. k. a.
